Gradient-Free Aeroacoustic Shape Optimization Using Large Eddy Simulation
Bibliographic record
Abstract
We present an aeroacoustic shape optimization framework that relies on high-order flux reconstruction, the gradient-free Mesh Adaptive Direct Search optimization algorithm, and large eddy simulation. Our parallel implementation ensures consistent runtime for each optimization iteration, regardless of the number of design parameters, provided that sufficient resources are available. The objective is to minimize the overall sound pressure level (OASPL) at a near-field observer by computing it directly from the flowfield. We evaluate this framework across three problems. First, an open deep cavity is considered at a freestream Mach number of [Formula: see text] and Reynolds number of [Formula: see text], reducing the OASPL by 12.9 dB. Next, we considered tandem cylinders at [Formula: see text] and [Formula: see text], achieving over 11 dB of noise reduction by optimizing cylinder spacing and diameter ratio. Lastly, a baseline NACA0012 airfoil at [Formula: see text] and [Formula: see text] is optimized to generate a new four-digit NACA airfoil at an appropriate angle of attack to minimize the OASPL while ensuring the baseline time-averaged lift coefficient is maintained and prevents any increase in the baseline time-averaged drag coefficient. The OASPL and mean drag coefficient are reduced by 5.7 dB and more than 7%, respectively. These results highlight the feasibility and effectiveness of our aeroacoustic shape optimization framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".